U.S. patent application number 11/321045 was filed with the patent office on 2006-10-05 for non-intrusive speech-quality assessment.
This patent application is currently assigned to British Telecommunications public limited company. Invention is credited to Philip Gray, Michael P. Hollier.
Application Number | 20060224387 11/321045 |
Document ID | / |
Family ID | 8241721 |
Filed Date | 2006-10-05 |
United States Patent
Application |
20060224387 |
Kind Code |
A1 |
Gray; Philip ; et
al. |
October 5, 2006 |
Non-intrusive speech-quality assessment
Abstract
Non-intrusive speech-quality assessment uses vocal-tract models,
in particular for testing telecommunications systems and equipment.
This process requires reduction of the speech stream under
assessment into a set of parameters that are sensitive to the types
of distortion to be assessed. Once parameterized, the data is used
to generate a set of physiologically-based rules for error
identification, using a parametric modeling of the shape of the
vocal tract itself, by comparison between derived parameters and
the output of models of physiologically realistic forms for the
vocal tract, and the application of physical constraints on how
these can change over time.
Inventors: |
Gray; Philip; (Ipswich,
GB) ; Hollier; Michael P.; (St Martin, GB) |
Correspondence
Address: |
NIXON & VANDERHYE, PC
901 NORTH GLEBE ROAD, 11TH FLOOR
ARLINGTON
VA
22203
US
|
Assignee: |
British Telecommunications public
limited company
London
GB
|
Family ID: |
8241721 |
Appl. No.: |
11/321045 |
Filed: |
December 30, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10110100 |
Apr 8, 2002 |
|
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|
PCT/GB00/04145 |
Oct 26, 2000 |
|
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11321045 |
Dec 30, 2005 |
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Current U.S.
Class: |
704/261 ;
704/233; 704/E19.002 |
Current CPC
Class: |
G10L 25/69 20130101 |
Class at
Publication: |
704/261 ;
704/233 |
International
Class: |
G10L 13/00 20060101
G10L013/00 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 8, 1999 |
EP |
99308858.2 |
Claims
1. A method for identifying distortion in a signal carrying speech,
said method comprising: analyzing a signal according to parameters
derived from a set of physiologically-based rules using a
parametric model of the human vocal tract; and identifying parts of
the signal which could not have been generated by the human vocal
tract based on said analysis.
2. A method according to claim 1, in which the analysis of the
signal comprises identification of the instantaneous configuration
of the parametric model.
3. A method according to claim 1 in which the analysis of the
signal comprises the analysis of sequences of configurations of the
parametric model.
4. A method according to claim 1, in which cavity tracking and
context based error spotting are used to identify signal
errors.
5. A method according to claim 4, in which the parametric model
comprises a series of cylindrical tubes, the dimensions of the
tubes being derived from reflection coefficients determined from
analysis of the original signal.
6. A method according to claim 5, wherein the number of tubes in
the series is determined from a preliminary analysis of the signal
to identify vocal characteristics characteristic of the talker
generating the signal.
7. A method according to claim 1, in which pitch-synchronized
frames are selected for analysis.
8. Apparatus for assessing the quality of a signal carrying speech,
comprising processing means for performing the method of claim
1.
9. A data carrier carrying program data for programming a computer
to perform the method of claim 1.
10. Apparatus for assessing the quality of a signal carrying
speech, said apparatus comprising: means for deriving parameters of
a signal from a set of physiologically-based rules using a
parametric model of the human vocal tract, and means for
identifying parameters which indicate whether the signal could have
been generated by the human vocal tract.
11. Apparatus according to claim 10, comprising means for
identification of the instantaneous configuration of the parametric
model.
12. Apparatus according to claim 10 comprising means for analysis
of sequences of configurations of the parametric model.
13. Apparatus method according to claim 10, wherein the
parameter-deriving means include cavity tracking means and context
based error spotting means.
14. Apparatus according to claim 13, comprising means for analysis
of the original signal to identify reflection coefficients, and
model generation means for generation of a parametric model
comprising a series of cylindrical tubes, the dimensions of the
tubes being derived from the reflection coefficients.
15. Apparatus according to claim 14, comprising means for making a
preliminary analysis of the signal to identify vocal
characteristics characteristic of the talker generating the signal,
and wherein the parameteric model generation means is arranged to
select the number of tubes in the series according to the said
vocal characteristics.
16. Apparatus method according to claim 10, in which the analysis
means is arranged to select pitch-synchronized frames.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 10/110,100, filed Apr. 8, 2002, which is a National Phase of
International Application No. PCT/GB00/04145, filed Oct. 26, 2000
which designated the U.S., the contents of which are incorporated
herein.
BACKGROUND OF THE INVENTION
[0002] This invention relates to non-intrusive speech-quality
assessment using vocal-tract models, in particular for testing
telecommunications systems and equipment.
[0003] Customers are now able to choose a telecommunications
service provider based upon price and quality of service. The
decision is no longer fixed by monopolies or restricted by limited
technology. A range of services is available with differing costs
and quality of service. Service providers need the capability to
predict customers' perceptions of quality so that networks can be
optimized and maintained. Traditionally, networks have been
characterized using linear assessment techniques, tone-based
signals; and simple engineering metrics, such as signal-to-noise
ratio. As networks become more complex, including non-linear
elements such as echo cancellers and compressive speech coders,
there is a requirement for assessment systems which bear a closer
relationship to the human perception of signal quality. This role
has typically been filled with expensive and time-consuming
subjective tests using human subjects. Such tests are employed for
commissioning new network elements, during the design of new coding
algorithms, and for testing different network topologies.
[0004] Recent advances in perceptual modeling have led to the
construction of objective auditory models, which can generate
predictions of perceived telephony speech quality from a listener's
perspective. These assessment techniques require a known test
stimulus to excite a network connection and then use a
perceptually-motivated comparison between a reference version of
the known test stimulus, and a version of the same stimulus as
degraded by the system under test, to provide a measure of the
quality of the degraded version as it would be perceived by a human
listener.
[0005] FIG. 1 shows the principle of the BT Laboratories Perceptual
Analysis Measurement System (PAMS), disclosed in International
Patent Applications W094/00922, W095/01011, and W095/15035. In this
system the reference signal 11 comprises a speech-like test
stimulus which is used to excite the connection under test 10 to
generate a degraded signal 12. The two signals are then compared in
the analysis process 1 to generate an output 18 indicative of the
subjective impact of the degradation of the signal 12 when compared
with the reference signal 11.
[0006] Such assessment techniques are known as "intrusive" because
they require the withdrawal of the connection under test 10 from
normal service so that it can be excited with a known test stimulus
11. Removing a connection from normal service renders it
unavailable to customers and is expensive to the service provider.
In addition, the conditions that generate distortions and errors
could be due to network loading levels that are only present at
peak times. An out-of-hours assessment could therefore generate
artificial quality scores. This means that reliable intrusive
testing is relatively expensive in terms of capacity on a
customer's network connection.
[0007] In general, it would be preferable to continuously monitor
the quality of speech at a particular point in the network. In this
case, a "non-intrusive" solution is attractive, utilizing the
in-service signal to make predictions of quality. Given this
information, network traffic can be re-routed through less
congested parts of the network if quality drops. A fundamentally
different approach is required to analyse a degraded speech signal
without a reference signal. The entire process takes place
"downstream" of the equipment under test. Non-intrusive techniques
are discussed in International Patent Specifications W096/06495 and
W096/06496. Current non-intrusive assessment equipment performs
measurements such as echo, delay, noise and loudness in an attempt
to predict the clarity of a connection. However, a customer's
perception of speech quality is also affected by distortions and
irregularities in the speech structure, which are not described by
such simple measures.
[0008] International Patent Specification W097/05730 (now also U.S.
Pat. No. 6,035,270) describes a system of this general type which
aims to generate an output indicative of how plausible it is that
the passing audio stream was generated by the human vocal
production system. This is achieved by comparing the audio stream
with a spectral model representative of the sounds capable of
production by the human vocal system. This process requires pattern
recognition to distinguish the spectral characteristics
representative of speech and of distortion, so that their presence
can be identified.
[0009] These analysis processes use spectral models, although
physiological models 30 have previously been used for speech
synthesis--see for example the use of each types of model for these
respective purposes in International patent specifications
W096/06496 and W097/00432. Unlike a physiological model, spectral
models are empirical, and have no intrinsic basis on which to
identify what sounds the vocal tract is capable of producing.
However, the physiological articulatory models used in the
synthesis of continuous speech utilize constraints to ensure the
generated speech is smooth and natural sounding. These models would
therefore be unsuitable for an assessment process, since in such a
process the parameters generated must also be capable of
representing "illegal" vocal-tract shapes that the constraints used
by such a synthesis model would ordinarily remove. It is the
regions that are in error or distorted that contain the information
for such an assessment; to remove this at the parameterization
stage would make a subsequent analysis of their properties
redundant.
BRIEF SUMMARY OF THE INVENTION
[0010] According to exemplary embodiments of the present invention,
there is provided a method of identifying distortion in a signal
carrying speech, in which the signal is analyzed according to
parameters derived from a set of physiologically-based rules using
a parametric model of the human vocal tract, to identify parts of
the signal which could not have been generated by the human vocal
tract. This differs from the prior art systems described above
which use empirical spectral analysis rules to distinguish speech
from other signals. The analysis process used in the invention
instead considers whether physiological combinations exist that
could generate a given sound, in order to determine whether that
sound should be identified as possible to have been formed by a
human vocal tract.
[0011] Preferably the analysis process comprises the step of
reducing a speech stream into a set of parameters that are
sensitive to the types of distortion to be assessed.
[0012] Cavity tracking techniques and context based error spotting
may be used to identify signal errors. This allows both
instantaneous abnormalities and sequential errors to be identified.
Articulatory control parameters (parameters derived from the
movement of the individual muscles which control the vocal tract)
are extremely useful for speech synthesis applications where their
direct relationships with the speech production system can be
exploited. However, they are difficult to use for analysis, because
the articulatory control parameters are heavily constrained to
maintain their conformance to the production of real vocal tract
configurations. It is therefore difficult to model error
conditions, which necessarily require the modeling of conditions
that the vocal tract cannot produce. It is therefore preferred to
use acoustic tube models. Such models allow the derivation of
vocal-tract descriptors directly from the speech waveform, which is
attractive for the present analysis problem, as physiologically
unlikely conditions are readily identifiable.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] An embodiment of the invention will now be described, with
reference to the accompanying drawings, in which
[0014] FIG. 1 is a schematic illustration of the PAMS intrusive
assessment system already discussed.
[0015] FIG. 2 is a schematic illustration of the system according
to the invention.
[0016] FIG. 3 illustrates the use of a variable frame length.
[0017] FIG. 4 is an illustration of the pitch boundaries of a
voiced speech event.
[0018] FIG. 5 illustrates a simplified uniform-cross-sectional-area
tube model used in the invention.
[0019] FIG. 6 is an illustration of the human vocal tract.
[0020] FIG. 7 illustrates a cavity area sequence.
[0021] Non-intrusive speech quality assessment processes require
parameters with specific properties to be extracted from the speech
stream. They should be sensitive to the types of distortions that
occur in the network under test; they should be consistent across
talkers; and they should not generate ambiguous mappings between
speech events and parameters.
[0022] FIG. 2 shows illustratively the steps carried out by the
process of the invention. It will be understood that these may be
carried out by software controlling a general-purpose computer. The
signal generated by a talker 27 is degraded by the system 28 under
test. It is sampled at point 20 and concurrently transmitted to the
end user 29. The parameters and characteristics identified from the
process are used to generate an output 26 indicative of the
subjective impact of the degradation of the signal 27, compared
with the signal assumed to have been supplied by the source 27 to
the system 28 under test.
[0023] The degraded signal 27 is first sampled (step 20), and
several individual processes are then carried out on the sampled
signal.
DETAILED DESCRIPTION OF THE INVENTION
[0024] While the invention has been described in connection with
what is presently considered to be the most practical and preferred
embodiment, it is to be understood that the invention is not to be
limited to the disclosed embodiment, but on the contrary, is
intended to cover various modifications and equivalent arrangements
included within the spirit and scope of the appended claims.
[0025] A major problem with non-intrusive speech-quality assessment
is lack of information concerning talker characteristics. In the
laboratory it is possible to generate talker-specific algorithms
with near-perfect error spotting capabilities. These work well
because prior knowledge of the talker has been used in development,
even though no reference was used. In the real world operation with
multiple talkers is necessary, and individual talker variation can
generate significant performance reductions.
[0026] The process of the present invention compensates for this
type of error by including talker characteristics in both the
parameterization stage and also the assessment phase of the
algorithm. The talker characteristics are restricted to those that
can be derived from the speech waveform itself, but still yield
performance improvements.
[0027] A model is used in which the overall shape of the human
vocal tract is described for each pitch cycle. This approach
assumes that the speech to be analyzed is voiced, (i.e. the vocal
chords are vibrating, for example vowel sounds) so that the driving
stimulus can be assumed to be impulsive. The vocal characteristics
of the individual talker 27 are first identified (process 21 ).
These are features that are invariant for that talker 27, such as
the average fundamental frequency fo of the voice, which depends on
the length of the vocal tract. This process 21 is carried out as
follows. It uses a section of speech in the order of 10 seconds to
characterize the talker by extracting information about the
fundamental frequency and the third formant (third harmonic)
values. These values are calculated for the voiced sections of
speech only. The mean and standard deviation of the fundamental
frequency is used later, during the pitch-cycle identification. The
mean of the third formant values is used to estimate the length of
the vocal tract.
[0028] The number of tubes used to calculate vocal tract, measured
(as deviations from a notional figure of 17 cm) according to
information from the formant positions within the speech waveform.
Using the third formant, which is generally present with telephony
bandwidth restrictions, it is possible to alter the number of tubes
to populate the equivalent lossless tube model.
[0029] The appropriate number of tube sections is given by the
closest integer value to N.sub.t, where: N.sub.t=2lf.sub.s/c where:
l=vocal tract length; f.sub.s=sample frequency; c=speed of sound:
(330 m/sec). Assuming a sampling frequency of 16 kHz, for the
average talker of vocal tract length 17 cm and average 3.sup.rd
formant frequency of 2500 Hz, this leads to sixteen cross-sectional
areas being required to populate the tube model. Using a direct
proportionality between the average 3.sup.rd formant frequency for
a talker and the length of the vocal tract it is possible to
estimate the value of l in the equation above: this estimated value
l.sub.m is calculated from: l.sub.m/17=2500/d where d, average
3.sup.rd formant value.
[0030] For a female talker with an average third formant frequency
of 3 kHz, this gives an estimated vocal tract length of 14 cm, and
the number of tube sections N.sub.t as fourteen. This method for
vocal tract length normalization reduces the variation in the
parameters extracted from the speech stream so that a general set
of error identification rules can be used which are not affected by
variations between talker, of which pitch is the main concern.
[0031] Once characterization has been carried out using the initial
ten second section of speech, the parameters identified (mean
fundamental frequency, standard deviation, and vocal tract length)
may be used for the rest of the speech stream, periodically
repeating the initial process in order to detect changes in the
talker 27.
[0032] The samples taken from the signal 2 (step 20) are next used
to generate speech parameters from these characteristics. An
initial stage of pitch synchronization is carried out (step 22).
This stage generates a pitch-labeled speech stream, enabling the
extraction of parameters from the voiced sections of speech on a
variable time base. This allows synchronization with the speech
waveform production system, namely the human speech organs,
allowing parameters to be derived from whole pitch-periods. This is
achieved by selecting the number of samples in each frame such that
the frame length corresponds with a cycle of the talker's speech,
as shown in FIG. 3. Thus, if the talker's speech rises and falls in
pitch the frame length will track it. This reduces the dependence
of the parameterization on gross physical talker properties such as
their average fundamental frequency. Note that the actual sampling
rate carried out in the sampling step 20 remains constant at 16
kHz--it is the number of such samples going to make up each frame
which is varied.
[0033] Various methods exist for the generation of
pitch-synchronous boundaries for parameterization. The present
embodiment uses a hybrid temporal spectral method, as described by
the inventors in their paper "Constraint-based pitch-cycle
identification using a hybrid temporal spectral method"
--105.sup.th AES Convention, 1998. This process uses the mean
fundamental frequency f.sub.0, and the standard deviation of this
value, to constrain the search for these boundaries.
[0034] The output of this non-real time method can be seen in FIG.
4, which shows the pitch boundaries (marked "X") for a voiced
speech event. It can be seen that these are synchronized with the
largest peaks in the voice signal, and thus occur at the same
frequency as the fundamental frequency of the talker's voice. The
lengths of the pitch cycles vary to track changes in the pitch of
the talker's voice.
[0035] Having identified the pitch-synchronous parameters, the
parameterization of the vocal tract can now be done (step 23). It
is important that no constraints are imposed during the
parameterization stages that could smooth out or remove signal
errors, as they would then not be available for identification in
the error identification stage. Articulatory models used in the
synthesis of continuous speech utilize constraints to ensure the
generated speech is smooth and natural sounding. The parameters
generated by a non-intrusive assessment must be capable of
representing illegal vocal-tract shapes that would ordinarily be
removed by constraints if a synthesis model were used. It is the
regions that are in error or distorted that contain the information
for such an assessment, to remove this at the parameterization
stage would make a subsequent analysis of their properties
redundant.
[0036] In the process of the present embodiment, reflection
coefficients are first calculated directly from the speech waveform
over the period of a pitch cycle, and these are used to determine
the magnitude of each change in cross section area of the vocal
tract model, using the number of individual tube elements derived
from the talker characteristics already derived (step 21). The
diameters of the tubes to be used in the model can then be derived
from these boundary conditions (step 23). An illustration of this
representation can be seen in FIG. 5, which shows a simplified
uniform-cross-sectional-area model of a vocal tract. In this model
the vocal tract is modeled as a series of cylindrical tubes having
uniform length, and having individual cross sectional areas
selected to correspond with the various parts of the vocal tract.
The number of such tubes was determined in the preliminary step
21.
[0037] For comparison, the true shape of the human vocal tract is
illustrated in FIG. 6. In the left part of FIG. 6 there is shown a
cross section of a side view of the lower head and throat, with six
section lines numbered 1 to 6. In the right part of FIG. 6 are
shown the views taken on these section lines. The non-circular
shape of the real vocal tract, and the fact that the real
transitions are not abrupt steps result in higher harmonics being
modeled less well in the tube model of FIG. 5, but these do not
affect the analysis for present purposes. We can therefore use a
uniform-cross-sectional-area tube model to describe the
instantaneous state of the vocal tract.
[0038] Certain errors may be apparent from the individual vocal
tract parameters themselves, and can be identified directly.
However, more generalized error indentification rules may be
derived from parameters derived by aggregating these terms. For
this reason, dimensionality of the vocal-tract description is
reduced even further at this point to maintain a constant number
(step 24). Methods that track constrictions within the tract yield
large variations in the individual cavity parameters during
steady-state clean speech attributable to minor differences in the
calculation of the constriction point. These differences are
significant enough to mask certain errors in degraded speech
streams.
[0039] It has been found experimentally that the best results are
produced by splitting the tract into three regions: front cavity,
rear cavity, and jaw opening. The accompanying table shows the
number of tube elements making up each of the three cavities for
each of the numbers of tubes considered. TABLE-US-00001 Total
Number of Jaw Tubes Rear Cavity Front Cavity Opening 12 5 5 2 13 5
6 2 14 6 5 3 15 6 6 3 16 7 6 3 17 7 7 3 18 8 7 3
[0040] The total cross sectional area in each of the tube subsets
is aggregated to give an indication of cavity opening in each
case.
[0041] Examples of cavity traces can be seen in FIG. 7, showing (in
the lower part of the figure) the variation in area in each of the
three defined cavities during the passage of speech "He was
genuinely sorry to see them go", whose analogue representation is
indicated in the part of the Figure. The blank sections correspond
to unvoiced sounds and silences, which are not modeled using this
system. This is because the cross sectional area parameters can
only be calculated during a pitched voice event, such as those
which involve glottal excitation caused by vibration of the vocal
chords. Under these conditions parameters can be extracted from the
speech waveform which describes its state. The rest of the events
are unvoiced and are caused by constrictions at different places in
the tract causing turbulent airflow, or even a complete closure.
The state of the articulators is not so easy to estimate for such
events.
[0042] The cavity sizes extracted (step 24) from the vocal tract
parameters for each pitch frame are next assessed for physiological
violations (step 25). Any such violations are taken to be caused by
degradation of the signal 27, and cause an error to be identified.
These errors are identified in the output 26. Errors can be
categorized in two major classes, instantaneous and sequential.
[0043] Instantaneous errors are identified where the size of the
cavity value at a given instance in time is assessed as implying a
shape that would be impossible for a human vocal tract to take. An
extreme example of this is that certain signal distortions can
yield excessively large apparent jaw openings--for example 30 cm,
and could not have been produced by a human vocal tract. There are
other more subtle situations, which have been found empirically,
where certain combinations of cavity sizes do not occur in human
speech. Any such physiological impossibilities are labeled
accordingly, as being indicative of a signal distortion.
[0044] One of the most common areas of degradation in speech
streams in the modern telephony network is through speech coding.
Specialized coding schemes, specific to voice signals, can generate
distortions when incorrect outputs are generated from the coded
parameter stream. In this situation the individual frames may seem
entirely appropriate when viewed in isolation, but when the
properties of the adjacent frames are taken into account, an error
in the degraded signal is apparent. These types of distortion have
been termed "sequential errors". Sequential errors occur quite
often in heavily coded speech streams. If incorrect parameters
arrive at the decoder, because of miscoding or corruption during
transmission, the reconstructed speech stream may contain a
spurious speech event. This event may be "legal" --that is, if
viewed in isolation or over a short time period it does not require
a physiologically impossible instantaneous configuration of the
vocal tract -but when heard would be an obvious that an error was
present. These types of distortion are identified in the error
identification step by assessing the sizes of cavities and vocal
tract parameters, in conjunction with the values for preceding and
subsequent frames, to identify sequences of cavity sizes which are
indicative of signal distortion.
[0045] The error identification process 25 operates according to
predetermined rules arranged to identify individual cavity values,
or sequences of such values, which cannot occur physiologically.
Some speech events are capable of generation by more than
configuration of the vocal tract. This may result in apparent
sequential errors when the process responds to a sequence including
such an event, if the process selects a vocal tract configuration
different from that actually used by the talker. The process is
arranged to identify any apparent sequential errors which could
result from such ambiguities, so that it can avoid mislabeling them
as errors.
[0046] While the invention has been described in connection with
what is presently considered to be the most practical and preferred
embodiment, it is to be understood that the invention is not to be
limited to the disclosed embodiment, but on the contrary, is
intended to cover various modifications and equivalent arrangements
included within the spirit and scope of the appended claims.
* * * * *